To delineate the molecular mechanism underlying the inverse agonist activity of olmesartan, a potent angiotensin II type 1 (AT 1 ) receptor antagonist, we performed binding affinity studies and an inositol phosphate production assay. 257 was found to be important for the interaction with olmesartan but not for the inverse agonist activity. Based on these results, we constructed a model for the interaction between olmesartan and the AT 1 receptor. Although the activation of G protein-coupled receptors is initiated by anti-clockwise rotation of transmembrane (TM) III and TM VI followed by changes in the conformation of the receptor, in this model, cooperative interactions between the hydroxyl group and Tyr 113 in TM III and between the carboxyl group and His 256 in TM VI were essential for the potent inverse agonist activity of olmesartan. We speculate that the specific interaction of olmesartan with these two TMs is essential for stabilizing the AT 1 receptor in an inactive conformation. A better understanding of the molecular mechanisms of the inverse agonism could be useful for the development of new G proteincoupled receptor antagonists with inverse agonist activity.Angiotensin II (Ang II) 3 receptor antagonists (ARBs) are highly selective for the Ang II type 1 (AT 1 ) receptor, which is a member of the G protein-coupled receptor (GPCR) superfamily, and block the diverse effects of Ang II. In addition to their blood pressure-lowering effects in hypertensive patients, ARBs have been shown to promote regression of left ventricular hypertrophy and decrease cardiovascular morbidity and mortality in patients with heart failure or hypertensive diabetic nephropathy with proteinuria (1). Many ARBs are available for clinical use. Because not all ARBs have the same effects, some benefits conferred by ARBs may not be class effects (2). This notion represents an exciting new area in ARB-based therapy, which holds the promise of reducing the incidence of cardiovascular disease.Inverse agonists, such as the opioid receptor ligand ICI174864 (3), block agonist-independent signal transduction by GPCRs. Many clinically important medications have been shown to behave as inverse agonists when tested against either wild-type (WT) or mutated GPCRs; e.g. olanzapine in the 5-hydroxytryptamine 2C receptors (4) and metoprolol in the ␤-adrenoreceptor (5). Spontaneous receptor mutations leading to constitutive activity have been implicated in some human diseases (6, 7). However, such spontaneous mutations have not been reported for the AT 1 receptor, and the WT AT 1 receptor shows slight constitutive activity (2). A recent study demonstrates that the WT AT 1 receptor is activated by mechanical stretching of cultured rat myocytes without the involvement of Ang II, and this was suppressed by an inverse agonist (8). The same study also demonstrates that cardiac hypertrophy induced by constricting the transverse aorta in angiotensinogen knock-out mice was attenuated by an inverse agonist, suggesting that the WT AT 1 receptor is activated ind...

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Acceptance of novelty depends on the receiver's emotional state. This paper proposes a novel mathematical model for predicting emotions elicited by the novelty of an event under different conditions. It models two emotion dimensions, arousal and valence, and considers different uncertainty levels. A state transition from before experiencing an event to afterwards is assumed, and a Bayesian model estimates a posterior distribution as being proportional to the product of a prior distribution and a likelihood function. Our model uses Kullback-Leibler divergence of the posterior from the prior, which we termed information gain, to represent arousal levels because it corresponds to surprise, a high-arousal emotion, upon experiencing a novel event. Based on Berlyne's hedonic function, we formalized valence as a summation of reward and aversion systems that are modeled as sigmoid functions of information gain. We derived information gain as a function of prediction errors (i.e., differences between the mean of the posterior and the peak likelihood), uncertainty (i.e., variance of the prior that is proportional to prior entropy), and noise (i.e., variance of the likelihood function). This functional model predicted an interaction effect of prediction errors and uncertainty on information gain, which we termed the arousal crossover effect. This effect means that the greater the uncertainty, the greater the information gain for a small prediction error. However, for large prediction errors, greater uncertainty means a smaller information gain. To verify this effect, we conducted an experiment with participants who watched short videos in which different percussion instruments were played. We varied uncertainty levels by using familiar and unfamiliar instruments, and we varied prediction error magnitudes by including congruent or incongruent percussive sounds in the videos. Event-related potential P300 amplitudes and subjective reports of surprise in response to the percussive sounds were used as measures of arousal levels, and the findings supported the hypothesized arousal crossover effect. The concordance between our model's predictions and our experimental results suggests that Bayesian information gain can be decomposed into uncertainty and prediction errors and is a valid measure of emotional arousal. Our model's predictions of arousal may help identify positively accepted novelty.

A series of imidazole-5-carboxylic acids bearing alkyl, alkenyl, and hydroxyalkyl substituents at the 4-position and their related compounds were prepared and evaluated for their antagonistic activities to the angiotensin II (AII) receptor. Among them, the 4-(1-hydroxyalkyl)-imidazole derivatives had strong binding affinity to the AII receptor and potently inhibited the AII-induced pressor response by intravenous administration. Various esters of these acids showed potent and long-lasting antagonistic activity by oral administration. The most promising compounds were (5-methyl-2-oxo-1,3-dioxol-4-yl)methyl (CS-866) and (pivaloyloxy)-methyl esters of 4-(1-hydroxy-1-methylethyl)-2-propyl-1-[(2'-1H-tetrazol-5- ylbiphenyl-4-yl)-methyl]imidazole-5-carboxylic acid (26c). A study involving stereochemical comparison of 26c with the acetylated C-terminal pentapeptide of AII was also undertaken.

Prior expectation affects posterior perceptual experience. This contextual bias is called expectation effect. Previous studies have observed two different patterns of expectation effect: contrast and assimilation. Contrast magnifies the perceived incongruity, and assimilation diminishes the incongruity. This study proposes a computational model that explains the conditions of contrast and assimilation based on neural coding principles. This model proposed that prediction error, uncertainty, and external noise affected the expectation effect. Computer simulations with the model show that the pattern of expectation effect shifted from assimilation to contrast as the prediction error increased, uncertainty decreased the extent of the expectation effect, and external noise increased the assimilation. We conducted an experiment on the size–weight illusion (SWI) as a case of the cross‐modal expectation effect and discussed correspondence with the simulation. We discovered conditions where the participants perceived bigger object to be heavier than smaller one, which contradicts to conventional SWI. Practical applications Expectation effect in sensory perception represents a perceptual bias caused by prior expectation, such as illusions and cross‐modality. The computational model proposed in this study guides researchers and practitioners who investigate this bias in sensory studies to set a hypothesis with appropriate experimental factors. For example, the model suggests that prediction error can be used as a main factor to identify a condition at which assimilation switches over to contrast. The model provided how expectation uncertainty and noise of stimulus affect the switchover point of prediction error and extent of expectation effect. Uncertainty, which may differ from person to person, can be used as a factor to explain personal differences in the extent of expectation effect.